Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An effective and efficient algorithm for high-dimensional outlier detection
The VLDB Journal — The International Journal on Very Large Data Bases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Contrasting the Contrast Sets: An Alternative Approach
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
A correlation-based model for unsupervised feature selection
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Strong Compound-Risk Factors: Efficient Discovery Through Emerging Patterns and Contrast Sets
IEEE Transactions on Information Technology in Biomedicine
On the stimulation of patterns: definitions, calculation method and first usages
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Flexible and adaptive subspace search for outlier analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Contrast data mining is a key tool for finding differences between sets of objects, or classes, and contrast patterns are a popular method for discrimination between two classes. However, such patterns can be limited in two primary ways: i) They do not readily allow second order differentiation - i.e. discovering contrasts of contrasts, ii) Mining contrast patterns often results in an overwhelming volume of output for the user. To address these limitations, this paper proposes a method which can identify contrast behaviour across both classes and also groups of classes. Furthermore, to increase interpretability for the user, it presents a new technique for finding the attributes which represent the key underlying factors behind the contrast behaviour. The associated mining task is computationally challenging and we describe an efficient algorithm to handle it, based on binary decision diagrams. Experimental results demonstrate that our technique can efficiently identify and explain contrast behaviour which would be difficult or impossible to isolate using standard techniques.